Overview

Dataset statistics

Number of variables13
Number of observations157338
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.2 MiB
Average record size in memory128.0 B

Variable types

Categorical1
Numeric12

Alerts

DATETIME has a high cardinality: 157338 distinct valuesHigh cardinality
System Buy Price(GBP/MWh) is highly overall correlated with Market Index Price (£/MWh)High correlation
Market Index Price (£/MWh) is highly overall correlated with System Buy Price(GBP/MWh)High correlation
National demand (MW) is highly overall correlated with Total system demand (MW) and 1 other fieldsHigh correlation
Embbeded wind generation (MW) is highly overall correlated with Wind generation (MW)High correlation
Embbeded solar generation (MW) is highly overall correlated with Solar generation (MW)High correlation
Total system demand (MW) is highly overall correlated with National demand (MW) and 1 other fieldsHigh correlation
Wind generation (MW) is highly overall correlated with Embbeded wind generation (MW)High correlation
Solar generation (MW) is highly overall correlated with Embbeded solar generation (MW)High correlation
Hydro generation (MW) is highly overall correlated with National demand (MW) and 1 other fieldsHigh correlation
DATETIME is uniformly distributedUniform
DATETIME has unique valuesUnique
Embbeded solar generation (MW) has 73336 (46.6%) zerosZeros
Solar generation (MW) has 77767 (49.4%) zerosZeros
Biomass generation (MW) has 67010 (42.6%) zerosZeros

Reproduction

Analysis started2023-08-08 23:32:14.992749
Analysis finished2023-08-08 23:33:27.160143
Duration1 minute and 12.17 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

DATETIME
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct157338
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.4 MiB
2014-01-01 00:00:00
 
1
2020-01-02 07:00:00
 
1
2020-01-02 03:30:00
 
1
2020-01-02 04:00:00
 
1
2020-01-02 04:30:00
 
1
Other values (157333)
157333 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2989422
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique157338 ?
Unique (%)100.0%

Sample

1st row2014-01-01 00:00:00
2nd row2014-01-01 00:30:00
3rd row2014-01-01 01:00:00
4th row2014-01-01 01:30:00
5th row2014-01-01 02:00:00

Common Values

ValueCountFrequency (%)
2014-01-01 00:00:00 1
 
< 0.1%
2020-01-02 07:00:00 1
 
< 0.1%
2020-01-02 03:30:00 1
 
< 0.1%
2020-01-02 04:00:00 1
 
< 0.1%
2020-01-02 04:30:00 1
 
< 0.1%
2020-01-02 05:00:00 1
 
< 0.1%
2020-01-02 05:30:00 1
 
< 0.1%
2020-01-02 06:00:00 1
 
< 0.1%
2020-01-02 06:30:00 1
 
< 0.1%
2020-01-02 07:30:00 1
 
< 0.1%
Other values (157328) 157328
> 99.9%

Length

2023-08-08T23:33:27.463431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16:00:00 3278
 
1.0%
22:30:00 3278
 
1.0%
18:00:00 3278
 
1.0%
18:30:00 3278
 
1.0%
19:00:00 3278
 
1.0%
19:30:00 3278
 
1.0%
20:00:00 3278
 
1.0%
20:30:00 3278
 
1.0%
21:00:00 3278
 
1.0%
21:30:00 3278
 
1.0%
Other values (3316) 281896
89.6%

Most occurring characters

ValueCountFrequency (%)
0 1003481
33.6%
2 365450
 
12.2%
1 343658
 
11.5%
- 314676
 
10.5%
: 314676
 
10.5%
157338
 
5.3%
3 134949
 
4.5%
7 59478
 
2.0%
8 59478
 
2.0%
5 59432
 
2.0%
Other values (3) 176806
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2202732
73.7%
Dash Punctuation 314676
 
10.5%
Other Punctuation 314676
 
10.5%
Space Separator 157338
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1003481
45.6%
2 365450
 
16.6%
1 343658
 
15.6%
3 134949
 
6.1%
7 59478
 
2.7%
8 59478
 
2.7%
5 59432
 
2.7%
4 59096
 
2.7%
6 59048
 
2.7%
9 58662
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 314676
100.0%
Other Punctuation
ValueCountFrequency (%)
: 314676
100.0%
Space Separator
ValueCountFrequency (%)
157338
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2989422
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1003481
33.6%
2 365450
 
12.2%
1 343658
 
11.5%
- 314676
 
10.5%
: 314676
 
10.5%
157338
 
5.3%
3 134949
 
4.5%
7 59478
 
2.0%
8 59478
 
2.0%
5 59432
 
2.0%
Other values (3) 176806
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2989422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1003481
33.6%
2 365450
 
12.2%
1 343658
 
11.5%
- 314676
 
10.5%
: 314676
 
10.5%
157338
 
5.3%
3 134949
 
4.5%
7 59478
 
2.0%
8 59478
 
2.0%
5 59432
 
2.0%
Other values (3) 176806
 
5.9%

System Buy Price(GBP/MWh)
Real number (ℝ)

Distinct37768
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.108926
Minimum0
Maximum4037.8
Zeros718
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size614.7 KiB
2023-08-08T23:33:27.682220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.99
Q132.939999
median46.116501
Q370.119001
95-th percentile226.05
Maximum4037.8
Range4037.8
Interquartile range (IQR)37.179003

Descriptive statistics

Standard deviation89.360764
Coefficient of variation (CV)1.2745989
Kurtosis440.03067
Mean70.108926
Median Absolute Deviation (MAD)16.466501
Skewness13.146584
Sum11030798
Variance7985.3462
MonotonicityNot monotonic
2023-08-08T23:33:27.932232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 892
 
0.6%
65 720
 
0.5%
0 718
 
0.5%
40 705
 
0.4%
30 690
 
0.4%
55 685
 
0.4%
60 670
 
0.4%
45 639
 
0.4%
25 605
 
0.4%
20 501
 
0.3%
Other values (37758) 150513
95.7%
ValueCountFrequency (%)
0 718
0.5%
0.002000000095 1
 
< 0.1%
0.004999999888 2
 
< 0.1%
0.009999999776 7
 
< 0.1%
0.01099999994 1
 
< 0.1%
0.01999999955 1
 
< 0.1%
0.03999999911 5
 
< 0.1%
0.05000000075 22
 
< 0.1%
0.09000000358 4
 
< 0.1%
0.1000000015 25
 
< 0.1%
ValueCountFrequency (%)
4037.800049 2
< 0.1%
4035.97998 1
 
< 0.1%
4005.97998 2
< 0.1%
4000 2
< 0.1%
3929.310059 2
< 0.1%
3854.310059 1
 
< 0.1%
3763.75 1
 
< 0.1%
3750 1
 
< 0.1%
3738.800049 3
< 0.1%
3435.97998 1
 
< 0.1%
Distinct24359
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.942788
Minimum0
Maximum1983.66
Zeros223
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size614.7 KiB
2023-08-08T23:33:28.197848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.280001
Q135.5225
median44.650002
Q362.5675
95-th percentile211.29299
Maximum1983.66
Range1983.66
Interquartile range (IQR)27.045

Descriptive statistics

Standard deviation70.671211
Coefficient of variation (CV)1.0401577
Kurtosis43.116058
Mean67.942788
Median Absolute Deviation (MAD)11.32
Skewness4.4347601
Sum10689982
Variance4994.4199
MonotonicityNot monotonic
2023-08-08T23:33:28.432239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 223
 
0.1%
36.75 68
 
< 0.1%
35.52000046 68
 
< 0.1%
35.02999878 66
 
< 0.1%
35.81999969 64
 
< 0.1%
37.49000168 63
 
< 0.1%
40 63
 
< 0.1%
37.15999985 63
 
< 0.1%
35.68999863 62
 
< 0.1%
37.54000092 62
 
< 0.1%
Other values (24349) 156536
99.5%
ValueCountFrequency (%)
0 223
0.1%
0.009999999776 4
 
< 0.1%
0.01999999955 3
 
< 0.1%
0.02999999933 3
 
< 0.1%
0.05000000075 1
 
< 0.1%
0.0700000003 3
 
< 0.1%
0.07999999821 3
 
< 0.1%
0.1000000015 2
 
< 0.1%
0.1099999994 2
 
< 0.1%
0.1299999952 1
 
< 0.1%
ValueCountFrequency (%)
1983.660034 1
< 0.1%
1893.02002 1
< 0.1%
1837.890015 1
< 0.1%
1817.359985 1
< 0.1%
1815.369995 1
< 0.1%
1795.97998 1
< 0.1%
1699.73999 1
< 0.1%
1676.839966 1
< 0.1%
1561.589966 1
< 0.1%
1557.209961 1
< 0.1%

Market Index Volume (MWh)
Real number (ℝ)

Distinct37760
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean872.35283
Minimum0
Maximum3743.3501
Zeros221
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size614.7 KiB
2023-08-08T23:33:28.682225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile258.3925
Q1504.96251
median784.09998
Q31157.2
95-th percentile1779.3575
Maximum3743.3501
Range3743.3501
Interquartile range (IQR)652.23744

Descriptive statistics

Standard deviation479.34305
Coefficient of variation (CV)0.549483
Kurtosis0.78887981
Mean872.35283
Median Absolute Deviation (MAD)313.34998
Skewness0.89833122
Sum1.3725425 × 108
Variance229769.75
MonotonicityNot monotonic
2023-08-08T23:33:28.916599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 221
 
0.1%
312.5 28
 
< 0.1%
473 28
 
< 0.1%
424.5 25
 
< 0.1%
467 25
 
< 0.1%
371.5 25
 
< 0.1%
422.5 24
 
< 0.1%
328 24
 
< 0.1%
418.5 24
 
< 0.1%
418 22
 
< 0.1%
Other values (37750) 156892
99.7%
ValueCountFrequency (%)
0 221
0.1%
25 14
 
< 0.1%
25.79999924 1
 
< 0.1%
26 2
 
< 0.1%
26.75 1
 
< 0.1%
26.95000076 1
 
< 0.1%
27 1
 
< 0.1%
27.45000076 1
 
< 0.1%
27.5 2
 
< 0.1%
28.20000076 1
 
< 0.1%
ValueCountFrequency (%)
3743.350098 1
< 0.1%
3662.649902 1
< 0.1%
3660.899902 1
< 0.1%
3658.649902 1
< 0.1%
3594.399902 1
< 0.1%
3537.099854 1
< 0.1%
3474 1
< 0.1%
3452.300049 1
< 0.1%
3431.649902 1
< 0.1%
3427.100098 1
< 0.1%

Settlement Period
Real number (ℝ)

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.499123
Minimum1
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-08-08T23:33:29.182220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q112
median24
Q336
95-th percentile46
Maximum48
Range47
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.852979
Coefficient of variation (CV)0.56544795
Kurtosis-1.201023
Mean24.499123
Median Absolute Deviation (MAD)12
Skewness1.5164132 × 10-5
Sum3854643
Variance191.90502
MonotonicityNot monotonic
2023-08-08T23:33:29.416602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 3278
 
2.1%
36 3278
 
2.1%
27 3278
 
2.1%
28 3278
 
2.1%
29 3278
 
2.1%
30 3278
 
2.1%
31 3278
 
2.1%
32 3278
 
2.1%
33 3278
 
2.1%
34 3278
 
2.1%
Other values (38) 124558
79.2%
ValueCountFrequency (%)
1 3278
2.1%
2 3278
2.1%
3 3278
2.1%
4 3278
2.1%
5 3278
2.1%
6 3278
2.1%
7 3278
2.1%
8 3278
2.1%
9 3278
2.1%
10 3278
2.1%
ValueCountFrequency (%)
48 3275
2.1%
47 3275
2.1%
46 3278
2.1%
45 3278
2.1%
44 3278
2.1%
43 3278
2.1%
42 3278
2.1%
41 3278
2.1%
40 3278
2.1%
39 3278
2.1%

National demand (MW)
Real number (ℝ)

Distinct30155
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29932.852
Minimum13367
Maximum79138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.7 KiB
2023-08-08T23:33:29.650969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13367
5-th percentile19605
Q124318
median29218
Q334847.75
95-th percentile42730
Maximum79138
Range65771
Interquartile range (IQR)10529.75

Descriptive statistics

Standard deviation7182.9297
Coefficient of variation (CV)0.2399681
Kurtosis0.26846182
Mean29932.852
Median Absolute Deviation (MAD)5212
Skewness0.52661043
Sum4.709575 × 109
Variance51594480
MonotonicityNot monotonic
2023-08-08T23:33:29.885329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27344 21
 
< 0.1%
29646 21
 
< 0.1%
25873 20
 
< 0.1%
27854 20
 
< 0.1%
29149 19
 
< 0.1%
30329 19
 
< 0.1%
25190 19
 
< 0.1%
26210 19
 
< 0.1%
29604 19
 
< 0.1%
25847 19
 
< 0.1%
Other values (30145) 157142
99.9%
ValueCountFrequency (%)
13367 1
< 0.1%
13394 1
< 0.1%
13597 1
< 0.1%
13737 1
< 0.1%
13745 1
< 0.1%
13923 1
< 0.1%
14061 1
< 0.1%
14075 1
< 0.1%
14261 1
< 0.1%
14389 1
< 0.1%
ValueCountFrequency (%)
79138 1
< 0.1%
78769 1
< 0.1%
78669 1
< 0.1%
78342 1
< 0.1%
78328 1
< 0.1%
77565 1
< 0.1%
77180 1
< 0.1%
77177 1
< 0.1%
77110 1
< 0.1%
77085 1
< 0.1%
Distinct4978
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1501.4337
Minimum83
Maximum5634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.7 KiB
2023-08-08T23:33:30.119726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile339
Q1731
median1282
Q32070
95-th percentile3398
Maximum5634
Range5551
Interquartile range (IQR)1339

Descriptive statistics

Standard deviation972.27356
Coefficient of variation (CV)0.64756344
Kurtosis0.56930178
Mean1501.4337
Median Absolute Deviation (MAD)630
Skewness0.97455847
Sum2.3623257 × 108
Variance945315.88
MonotonicityNot monotonic
2023-08-08T23:33:30.338485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502 132
 
0.1%
578 130
 
0.1%
580 125
 
0.1%
540 125
 
0.1%
552 124
 
0.1%
686 121
 
0.1%
940 119
 
0.1%
618 113
 
0.1%
494 112
 
0.1%
602 112
 
0.1%
Other values (4968) 156125
99.2%
ValueCountFrequency (%)
83 2
 
< 0.1%
86 2
 
< 0.1%
88 2
 
< 0.1%
90 6
< 0.1%
91 2
 
< 0.1%
92 2
 
< 0.1%
96 2
 
< 0.1%
97 6
< 0.1%
98 4
< 0.1%
100 2
 
< 0.1%
ValueCountFrequency (%)
5634 1
< 0.1%
5616 1
< 0.1%
5598 2
< 0.1%
5588 1
< 0.1%
5578 1
< 0.1%
5564 1
< 0.1%
5521 1
< 0.1%
5463 1
< 0.1%
5452 1
< 0.1%
5441 1
< 0.1%

Embbeded solar generation (MW)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5607
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1175.5372
Minimum0
Maximum13060
Zeros73336
Zeros (%)46.6%
Negative0
Negative (%)0.0%
Memory size614.7 KiB
2023-08-08T23:33:30.557225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q31810
95-th percentile5390
Maximum13060
Range13060
Interquartile range (IQR)1810

Descriptive statistics

Standard deviation1869.7026
Coefficient of variation (CV)1.5905091
Kurtosis2.6786849
Mean1175.5372
Median Absolute Deviation (MAD)23
Skewness1.7932217
Sum1.8495667 × 108
Variance3495788
MonotonicityNot monotonic
2023-08-08T23:33:30.807220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73336
46.6%
1 1536
 
1.0%
2 729
 
0.5%
3 465
 
0.3%
4 319
 
0.2%
5 242
 
0.2%
6 207
 
0.1%
7 197
 
0.1%
1050 176
 
0.1%
1120 175
 
0.1%
Other values (5597) 79956
50.8%
ValueCountFrequency (%)
0 73336
46.6%
1 1536
 
1.0%
2 729
 
0.5%
3 465
 
0.3%
4 319
 
0.2%
5 242
 
0.2%
6 207
 
0.1%
7 197
 
0.1%
8 171
 
0.1%
9 149
 
0.1%
ValueCountFrequency (%)
13060 1
< 0.1%
12910 1
< 0.1%
12800 2
< 0.1%
12500 1
< 0.1%
12480 1
< 0.1%
12430 1
< 0.1%
12310 1
< 0.1%
12300 1
< 0.1%
12260 1
< 0.1%
11980 1
< 0.1%

Total system demand (MW)
Real number (ℝ)

Distinct29414
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31373.069
Minimum16629
Maximum80820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.7 KiB
2023-08-08T23:33:31.041604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16629
5-th percentile21601
Q126018
median30561
Q335899
95-th percentile44027
Maximum80820
Range64191
Interquartile range (IQR)9881

Descriptive statistics

Standard deviation6932.311
Coefficient of variation (CV)0.22096375
Kurtosis0.55050778
Mean31373.069
Median Absolute Deviation (MAD)4866
Skewness0.63173485
Sum4.936176 × 109
Variance48056936
MonotonicityNot monotonic
2023-08-08T23:33:31.275974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32717 24
 
< 0.1%
29696 20
 
< 0.1%
31021 20
 
< 0.1%
27529 20
 
< 0.1%
27615 19
 
< 0.1%
26586 19
 
< 0.1%
29967 19
 
< 0.1%
30467 19
 
< 0.1%
31788 18
 
< 0.1%
28366 18
 
< 0.1%
Other values (29404) 157142
99.9%
ValueCountFrequency (%)
16629 1
< 0.1%
16741 1
< 0.1%
16856 1
< 0.1%
16888 1
< 0.1%
16952 1
< 0.1%
16954 1
< 0.1%
17053 1
< 0.1%
17083 1
< 0.1%
17119 1
< 0.1%
17193 1
< 0.1%
ValueCountFrequency (%)
80820 1
< 0.1%
80722 1
< 0.1%
80623 1
< 0.1%
79934 1
< 0.1%
79828 1
< 0.1%
79535 1
< 0.1%
79203 1
< 0.1%
78547 1
< 0.1%
78406 1
< 0.1%
78151 1
< 0.1%

Wind generation (MW)
Real number (ℝ)

Distinct17422
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5688.662
Minimum103
Maximum20912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.7 KiB
2023-08-08T23:33:31.525961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile905
Q12432
median4734
Q37925
95-th percentile13973
Maximum20912
Range20809
Interquartile range (IQR)5493

Descriptive statistics

Standard deviation4067.1345
Coefficient of variation (CV)0.7149545
Kurtosis0.22244728
Mean5688.662
Median Absolute Deviation (MAD)2588
Skewness0.93888986
Sum8.9504271 × 108
Variance16541583
MonotonicityNot monotonic
2023-08-08T23:33:31.744717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1533 34
 
< 0.1%
2816 34
 
< 0.1%
1206 33
 
< 0.1%
1962 33
 
< 0.1%
1377 33
 
< 0.1%
2167 33
 
< 0.1%
2320 33
 
< 0.1%
1382 33
 
< 0.1%
955 32
 
< 0.1%
2075 32
 
< 0.1%
Other values (17412) 157008
99.8%
ValueCountFrequency (%)
103 1
< 0.1%
109 1
< 0.1%
115 1
< 0.1%
118 1
< 0.1%
119 1
< 0.1%
121 1
< 0.1%
124 1
< 0.1%
125 1
< 0.1%
127 1
< 0.1%
129 1
< 0.1%
ValueCountFrequency (%)
20912 1
< 0.1%
20890 1
< 0.1%
20845 1
< 0.1%
20821 1
< 0.1%
20818 1
< 0.1%
20807 1
< 0.1%
20774 1
< 0.1%
20758 1
< 0.1%
20731 1
< 0.1%
20703 1
< 0.1%

Solar generation (MW)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8411
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1157.4499
Minimum0
Maximum9892
Zeros77767
Zeros (%)49.4%
Negative0
Negative (%)0.0%
Memory size614.7 KiB
2023-08-08T23:33:31.979099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q31739
95-th percentile5417.15
Maximum9892
Range9892
Interquartile range (IQR)1739

Descriptive statistics

Standard deviation1870.2579
Coefficient of variation (CV)1.6158436
Kurtosis2.6346176
Mean1157.4499
Median Absolute Deviation (MAD)3
Skewness1.8055438
Sum1.8211085 × 108
Variance3497864.8
MonotonicityNot monotonic
2023-08-08T23:33:32.213470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77767
49.4%
1 460
 
0.3%
2 302
 
0.2%
3 284
 
0.2%
4 225
 
0.1%
5 207
 
0.1%
9 199
 
0.1%
6 183
 
0.1%
8 163
 
0.1%
10 158
 
0.1%
Other values (8401) 77390
49.2%
ValueCountFrequency (%)
0 77767
49.4%
1 460
 
0.3%
2 302
 
0.2%
3 284
 
0.2%
4 225
 
0.1%
5 207
 
0.1%
6 183
 
0.1%
7 157
 
0.1%
8 163
 
0.1%
9 199
 
0.1%
ValueCountFrequency (%)
9892 1
< 0.1%
9888 1
< 0.1%
9821 1
< 0.1%
9797 1
< 0.1%
9731 1
< 0.1%
9728 1
< 0.1%
9708 1
< 0.1%
9697 1
< 0.1%
9694 2
< 0.1%
9691 1
< 0.1%

Hydro generation (MW)
Real number (ℝ)

Distinct1317
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.49401
Minimum0
Maximum1403
Zeros65
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size614.7 KiB
2023-08-08T23:33:32.440639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72
Q1208
median386
Q3594
95-th percentile888
Maximum1403
Range1403
Interquartile range (IQR)386

Descriptive statistics

Standard deviation254.49728
Coefficient of variation (CV)0.60812646
Kurtosis-0.40072376
Mean418.49401
Median Absolute Deviation (MAD)191
Skewness0.5381341
Sum65845011
Variance64768.871
MonotonicityNot monotonic
2023-08-08T23:33:32.659342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141 283
 
0.2%
121 281
 
0.2%
102 279
 
0.2%
130 278
 
0.2%
156 273
 
0.2%
230 272
 
0.2%
58 272
 
0.2%
158 271
 
0.2%
113 271
 
0.2%
64 266
 
0.2%
Other values (1307) 154592
98.3%
ValueCountFrequency (%)
0 65
< 0.1%
11 10
 
< 0.1%
12 45
< 0.1%
13 55
< 0.1%
14 3
 
< 0.1%
15 2
 
< 0.1%
16 21
 
< 0.1%
17 2
 
< 0.1%
18 46
< 0.1%
19 57
< 0.1%
ValueCountFrequency (%)
1403 1
 
< 0.1%
1397 3
< 0.1%
1386 1
 
< 0.1%
1382 1
 
< 0.1%
1379 1
 
< 0.1%
1377 1
 
< 0.1%
1376 2
< 0.1%
1375 1
 
< 0.1%
1374 1
 
< 0.1%
1371 1
 
< 0.1%

Biomass generation (MW)
Real number (ℝ)

Distinct2966
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1110.0334
Minimum0
Maximum3262
Zeros67010
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size614.7 KiB
2023-08-08T23:33:32.909390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1127
Q32083
95-th percentile2917
Maximum3262
Range3262
Interquartile range (IQR)2083

Descriptive statistics

Standard deviation1077.2351
Coefficient of variation (CV)0.97045292
Kurtosis-1.4591405
Mean1110.0334
Median Absolute Deviation (MAD)1127
Skewness0.25390676
Sum1.7465043 × 108
Variance1160435.4
MonotonicityNot monotonic
2023-08-08T23:33:33.143719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67010
42.6%
2048 127
 
0.1%
681 127
 
0.1%
2044 122
 
0.1%
2046 121
 
0.1%
2045 114
 
0.1%
2043 111
 
0.1%
1764 110
 
0.1%
2047 108
 
0.1%
2012 108
 
0.1%
Other values (2956) 89280
56.7%
ValueCountFrequency (%)
0 67010
42.6%
74 1
 
< 0.1%
81 2
 
< 0.1%
82 3
 
< 0.1%
83 2
 
< 0.1%
84 2
 
< 0.1%
85 1
 
< 0.1%
87 2
 
< 0.1%
90 2
 
< 0.1%
91 1
 
< 0.1%
ValueCountFrequency (%)
3262 1
 
< 0.1%
3204 1
 
< 0.1%
3177 1
 
< 0.1%
3169 2
< 0.1%
3168 1
 
< 0.1%
3166 1
 
< 0.1%
3161 2
< 0.1%
3160 4
< 0.1%
3159 4
< 0.1%
3158 4
< 0.1%

Interactions

2023-08-08T23:33:19.861702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:28.152254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:33.267516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:39.364113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:44.668001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:48.755284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:54.546605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:58.429430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:02.308131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:07.157356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:12.019870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:15.852494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:20.196385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:28.648324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:33.955937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:39.669061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:45.245325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:49.113653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:54.878821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:58.757427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:02.657239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:07.635573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:12.375295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:16.229242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:20.542738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:29.198929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:34.417518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:39.986540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:45.587773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:49.530089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:55.223345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:59.097047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:02.998637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:08.122475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:12.695525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:16.565038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:20.881804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:29.612201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:35.415776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:40.300131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:45.915500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:50.302763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:55.579776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:59.418604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:03.344478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:08.613590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:13.037910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:16.896314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:21.181361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:29.963964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:35.832034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:40.717835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:46.210544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:50.792417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:55.884370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:59.734706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:03.677605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:09.074129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:13.340077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:17.227581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:21.605552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:30.345825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:36.411405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:41.162436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:46.524174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:51.282448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:56.218843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:00.081178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:04.031233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:09.562817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:13.666370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:17.566458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:22.101171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:30.875766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:36.900520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:41.493984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:46.848955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:51.755790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:56.525801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:00.396256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:04.347425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:10.066624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:13.982607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:17.899127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:22.589380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:31.283916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:37.536004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:42.290077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:47.190436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:52.260118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:56.856285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:00.729374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:04.897084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:10.429951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:14.297277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:18.232786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:23.450091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:31.844467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:37.982565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:43.076067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:47.507958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:52.782154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:57.159065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:01.036602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:05.236082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:10.768239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:14.628539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:18.550576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:23.900956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:32.180155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:38.446183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:43.490478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:47.818874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:53.239809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:57.489605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:01.342535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:05.649864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:11.055419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:14.918861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:18.880789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:24.377166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:32.525728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:38.751444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:43.844008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:48.110382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:53.723926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:57.814894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:01.663880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:06.137777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:11.383505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:15.218611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:19.195784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:24.877524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:32.976923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:39.058347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:44.173385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:48.449002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:54.224106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:32:58.122046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:01.986729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:06.651010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:11.698619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:15.540481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-08T23:33:19.521779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-08-08T23:33:33.362513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
System Buy Price(GBP/MWh)Market Index Price (£/MWh)Market Index Volume (MWh)Settlement PeriodNational demand (MW)Embbeded wind generation (MW)Embbeded solar generation (MW)Total system demand (MW)Wind generation (MW)Solar generation (MW)Hydro generation (MW)Biomass generation (MW)
System Buy Price(GBP/MWh)1.0000.6850.2000.1100.146-0.0090.0120.1840.070-0.0100.0740.268
Market Index Price (£/MWh)0.6851.0000.2920.2160.257-0.0220.0640.2980.1380.0450.0840.360
Market Index Volume (MWh)0.2000.2921.0000.4550.2870.3150.2540.2960.3380.2100.2420.371
Settlement Period0.1100.2160.4551.0000.4760.0530.1220.4500.0360.0210.2040.034
National demand (MW)0.1460.2570.2870.4761.0000.0220.1970.988-0.0130.1360.513-0.095
Embbeded wind generation (MW)-0.009-0.0220.3150.0530.0221.0000.0300.0290.7960.0280.2640.181
Embbeded solar generation (MW)0.0120.0640.2540.1220.1970.0301.0000.173-0.0680.948-0.0030.048
Total system demand (MW)0.1840.2980.2960.4500.9880.0290.1731.0000.0090.1150.512-0.082
Wind generation (MW)0.0700.1380.3380.036-0.0130.796-0.0680.0091.000-0.0690.2430.330
Solar generation (MW)-0.0100.0450.2100.0210.1360.0280.9480.115-0.0691.000-0.0370.053
Hydro generation (MW)0.0740.0840.2420.2040.5130.264-0.0030.5120.243-0.0371.0000.030
Biomass generation (MW)0.2680.3600.3710.034-0.0950.1810.048-0.0820.3300.0530.0301.000

Missing values

2023-08-08T23:33:25.578852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-08T23:33:26.401042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DATETIMESystem Buy Price(GBP/MWh)Market Index Price (£/MWh)Market Index Volume (MWh)Settlement PeriodNational demand (MW)Embbeded wind generation (MW)Embbeded solar generation (MW)Total system demand (MW)Wind generation (MW)Solar generation (MW)Hydro generation (MW)Biomass generation (MW)
02014-01-01 00:00:0054.15700135.320000301.500000130008.01084.00.031920.04469.00.0768.00.0
12014-01-01 00:30:0055.49900137.279999365.649994230589.01079.00.032491.04601.00.0767.00.0
22014-01-01 01:00:0056.23099941.500000184.000000330306.01079.00.032521.04686.00.0708.00.0
32014-01-01 01:30:0052.89699936.740002185.149994429280.0931.00.031735.04774.00.0716.00.0
42014-01-01 02:00:0032.04000132.040001283.500000528174.0931.00.030661.04670.00.0702.00.0
52014-01-01 02:30:0030.51000030.510000322.500000627456.0994.00.029992.04683.00.0701.00.0
62014-01-01 03:00:0050.00000026.620001454.299988726391.0994.00.028888.04570.00.0701.00.0
72014-01-01 03:30:0027.60400024.410000566.950012825367.0950.00.027786.04665.00.0701.00.0
82014-01-01 04:00:0022.38999922.389999554.400024924500.0950.00.026798.04800.00.0703.00.0
92014-01-01 04:30:0020.75000020.750000666.6500241023943.0963.00.026264.04730.00.0704.00.0
DATETIMESystem Buy Price(GBP/MWh)Market Index Price (£/MWh)Market Index Volume (MWh)Settlement PeriodNational demand (MW)Embbeded wind generation (MW)Embbeded solar generation (MW)Total system demand (MW)Wind generation (MW)Solar generation (MW)Hydro generation (MW)Biomass generation (MW)
1573282023-01-01 19:00:00250.000000197.2799991897.6500243930330.0996.00.031475.07396.00.01028.01432.0
1573292023-01-01 19:30:00250.000000200.0299991808.5999764029100.0982.00.030275.07040.00.0856.01435.0
1573302023-01-01 20:00:00230.000000196.8899992324.5000004128532.0948.00.029486.07005.00.0785.01430.0
1573312023-01-01 20:30:00217.000000182.9199982281.3000494228164.0914.00.029156.06912.00.0730.01420.0
1573322023-01-01 21:00:00230.000000188.5700072592.6000984327163.0895.00.027978.06660.00.0547.01415.0
1573332023-01-01 21:30:00217.000000177.8999942474.4499514426018.0875.00.026756.06420.00.0529.01408.0
1573342023-01-01 22:00:0042.000000161.7599952339.3000494525146.0863.00.025754.06095.00.0501.01415.0
1573352023-01-01 22:30:00290.000000166.0000002236.5000004624139.0850.00.024790.05614.00.0520.01420.0
1573362023-01-01 23:00:00236.000000156.509995617.5499884722885.0917.00.023783.04856.00.0488.01352.0
1573372023-01-01 23:30:00151.350006151.350006969.2500004821971.0983.00.023151.04617.00.0453.01267.0